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The widespread use of technology has facilitated many changes in the education sector including higher education. Academic institutes are concentrating their efforts on measuring the level of student engagement and participation in online learning environments for student success. This paper analyses student log data to quantify the effectiveness of online presence on student performance in a blended course using frequency and duration as indicators. The analysis shows both frequency and duration having a statistically significant impact on the students’ final marks. The study proposes a multiple linear regression model using these measurements to predict the final mark of students in a blended learning environment. The predictive regression model, explained through the use of two new models; Online Measurable Presence Model (OMPM) and Slingshot model, can be used to determine the effectiveness of student online presence for success in a blended higher learning environment in the Pacific.
Sharma et al. (Tue,) studied this question.